{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T06:59:29Z","timestamp":1761893969382},"reference-count":0,"publisher":"Oxford University Press (OUP)","issue":"11","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2004,7,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: Due to the existence of the loss of synchrony in cell-cycle data sets, standard clustering methods (e.g. k-means), which group open reading frames (ORFs) based on similar expression levels, are deficient unless the temporal pattern of the expression levels of the ORFs is taken into account.<\/jats:p>\n               <jats:p>Methods: We propose to improve the performance of the k-means method by assigning a decreasing weight on its variable level and evaluating the \u2018weighted k-means\u2019 on a yeast cell-cycle data set. Protein complexes from a public website are used as biological benchmarks. To compare the k-means clusters with the structures of the protein complexes, we measure the agreement between these two ways of clustering via the adjusted Rand index.<\/jats:p>\n               <jats:p>Results: Our results show the time-decreasing weight function\u2014exp[\u2212(1\/2)(t2\/C2)\u2014]which we assign to the variable level of k-means, generally increases the agreement between protein complexes and k-means clusters when C is near the length of two cell cycles.<\/jats:p>","DOI":"10.1093\/bioinformatics\/bth169","type":"journal-article","created":{"date-parts":[[2004,6,1]],"date-time":"2004-06-01T00:33:47Z","timestamp":1086050027000},"page":"1766-1771","source":"Crossref","is-referenced-by-count":6,"title":["Correcting the loss of cell-cycle synchrony in clustering analysis of microarray data using weights"],"prefix":"10.1093","volume":"20","author":[{"given":"Fenghai","family":"Duan","sequence":"first","affiliation":[{"name":"Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT 06520-8034, USA"}]},{"given":"Heping","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT 06520-8034, USA"}]}],"member":"286","published-online":{"date-parts":[[2004,5,27]]},"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/20\/11\/1766\/48905878\/bioinformatics_20_11_1766.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/20\/11\/1766\/48905878\/bioinformatics_20_11_1766.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,25]],"date-time":"2023-01-25T16:46:10Z","timestamp":1674665170000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/20\/11\/1766\/300235"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2004,5,27]]},"references-count":0,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2004,7,22]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/bth169","relation":{},"ISSN":["1367-4811","1367-4803"],"issn-type":[{"value":"1367-4811","type":"electronic"},{"value":"1367-4803","type":"print"}],"subject":[],"published-other":{"date-parts":[[2004,7,22]]},"published":{"date-parts":[[2004,5,27]]}}}